Data driven an introduction to management consulting in the 21st century

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Management for Professionals Jeremy David Curuksu Data Driven An Introduction to Management Consulting in the 21st Century Management for Professionals More information about this series at http://www.springer.com/series/10101 Jeremy David Curuksu Data Driven An Introduction to Management Consulting in the 21st Century Jeremy David Curuksu Amazon Web Services, Inc New York, NY, USA Amazon Web Services, Inc is not affiliated with the writing and publication of this book nor to any material within ISSN 2192-8096    ISSN 2192-810X (electronic) Management for Professionals ISBN 978-3-319-70228-5    ISBN 978-3-319-70229-2 (eBook) https://doi.org/10.1007/978-3-319-70229-2 Library of Congress Control Number: 2017960847 © Springer International Publishing AG, part of Springer Nature 2018 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations Printed on acid-free paper This Springer imprint is published by the registered company Springer International Publishing AG part of Springer Nature The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Endorsements “Jeremy David Curuksu’s Data Driven is an extensive yet concise view on how the consulting industry will have to change in the age of data His thoroughly researched perspective is accessible to a wide range of readers – also those without a consulting and/or data science background However, this book is particularly relevant to anyone working in the management consulting industry, as it will shape our industry in the years to come.” –Christian Wasmer, Consultant, The Boston Consulting Group (BCG) “Jeremy’s book offers a comprehensive overview of the consulting industry–not only the type of cases and strategic approaches commonly used by the industry, but also applying these lenses on the industry itself to analyze the challenges it faces in the age of big data and where it might head next In addition, it gives a great overview of the popular data science techniques applicable to consulting The combination of the content will give consulting newcomers and veterans alike a new perspective on the industry and spark ideas on opportunities to combine the traditional consulting strengths and the new data science techniques in creative ways to offer distinctive value to clients It'll also give anyone curious about what the black box of management consulting is about a good inside view on how the industry works.” –Yuanjian Carla Li, Associate, McKinsey & Company “As consulting continues to go deeper into the business analytics space, this book provides great insights into both business frameworks and mathematical concepts to help you be a successful consultant There is certainly a lot of material out in the marketplace What I find to be most attractive about this book is that it doesn’t matter if you are an experienced consultant like myself or just starting your career; there is lots of useful content for both I personally found the statistical/analytical formulas as they pertain to business analytics paired with the business concepts to be extremely useful in getting a good understanding of the field Highly recommended to both experienced consultants and those looking to enter this rapidly changing field.” –Thevuthasan Senthuran, Senior Strategy Consultant, IBM Corp., Chief Analytics Office v Acknowledgments To the community of aspiring consultants at MIT, Annuschka Bork for her genuine suggestions, and my kind family vii Contents 1Analysis of the Management Consulting Industry ������������������������������    1 2Future of Big Data in Management Consulting������������������������������������   17 3Toolbox of Consulting Methods��������������������������������������������������������������   27 4The Client-Consultant Interaction ��������������������������������������������������������   43 5The Structure of Consulting Cases��������������������������������������������������������   61 6Principles of Data Science: Primer��������������������������������������������������������   73 7Principles of Data Science: Advanced����������������������������������������������������   87 8Principles of Strategy: Primer����������������������������������������������������������������  129 9Principles of Strategy: Advanced ����������������������������������������������������������  153 Conclusion��������������������������������������������������������������������������������������������������������  171 References ��������������������������������������������������������������������������������������������������������  173 Index������������������������������������������������������������������������������������������������������������������  183 ix Detailed Contents 1Analysis of the Management Consulting Industry ������������������������������    1 1.1Definition and Market Segments������������������������������������������������������     1 1.1.1The Value Proposition����������������������������������������������������������     1 1.1.2Industry Life Cycle ��������������������������������������������������������������     2 1.1.3Segmentation by Services ����������������������������������������������������     2 1.1.4Segmentation by Sectors������������������������������������������������������     4 1.1.5Segmentation by Geography������������������������������������������������     9 1.2Success Factors ��������������������������������������������������������������������������������    11 1.3Competitive Landscape��������������������������������������������������������������������    12 1.3.1Basis of Competition������������������������������������������������������������    12 1.3.2Emergence of New Information Technologies ��������������������    12 1.3.3Main Players ������������������������������������������������������������������������    13 1.4Operations and Value Network ��������������������������������������������������������    14 2Future of Big Data in Management Consulting������������������������������������   17 2.1General Outlooks in the Management Consulting Industry ������������    17 2.2Future of Big Data in Management Consulting��������������������������������    18 2.2.1Factors that Favor the Integration of Big Data in Management Consulting������������������������������������������    18 2.2.2Factors that Refrain the Transition����������������������������������������    21 2.3So What: A Scenario Analysis����������������������������������������������������������    22 3Toolbox of Consulting Methods��������������������������������������������������������������   27 3.1Organizational Development������������������������������������������������������������    28 3.1.1Strategic Planning ����������������������������������������������������������������    28 3.1.2Innovation ����������������������������������������������������������������������������    28 3.1.3Re-engineering����������������������������������������������������������������������    30 3.1.4Scenario Planning ����������������������������������������������������������������    30 3.1.5Brainstorming ����������������������������������������������������������������������    31 3.1.6Resource Allocation��������������������������������������������������������������    31 3.1.7Cost Optimization ����������������������������������������������������������������    32 3.1.8Downsizing ��������������������������������������������������������������������������    32 xi xii Detailed Contents 3.2Consumer Market Research����������������������������������������������������������������  33 3.2.1Documentary Research ����������������������������������������������������������  33 3.2.2Customer Segmentation����������������������������������������������������������  34 3.2.3Surveys������������������������������������������������������������������������������������  34 3.2.4Focus Group����������������������������������������������������������������������������  36 3.2.5Interviews��������������������������������������������������������������������������������  37 3.2.6Big Data Analytics������������������������������������������������������������������  38 3.2.7Pricing ������������������������������������������������������������������������������������  38 3.3Competitive Intelligence ��������������������������������������������������������������������  39 3.3.1Supply Chain Management����������������������������������������������������  39 3.3.2Due Diligence ������������������������������������������������������������������������  40 3.3.3Benchmarking ������������������������������������������������������������������������  40 3.3.4Outsourcing����������������������������������������������������������������������������  40 3.3.5Mergers and Acquisitions ������������������������������������������������������  41 4The Client-Consultant Interaction ��������������������������������������������������������   43 4.1Nature of the Relationship������������������������������������������������������������������  43 4.1.1The Big Picture: Theoretical Models��������������������������������������  43 4.1.2The Models in Practice ����������������������������������������������������������  46 4.2On the Client’s Expectations: Why Hire a Consultant?����������������������  49 4.3Ethical Standards��������������������������������������������������������������������������������  51 4.4The First Interview: Defining the Case and Objectives����������������������  52 4.4.1Goals of First Meetings����������������������������������������������������������  52 4.4.2Sample of Questions Consultant-to-Client ����������������������������  52 4.4.3Sample of Questions Client-to-Consultant And How to Respond��������������������������������������������������������������  53 4.5Working with the Client During the Engagement������������������������������  54 4.5.1Clarifying Objectives��������������������������������������������������������������  54 4.5.2Executing Consulting Activities����������������������������������������������  55 4.5.3Implementing the Recommendations ������������������������������������  55 4.6Stand and Deliver: Terminating the Assignment��������������������������������  55 4.6.1Preparing the Slides����������������������������������������������������������������  56 4.6.2Delivering the Presentation����������������������������������������������������  58 5The Structure of Consulting Cases��������������������������������������������������������   61 5.1How to Develop a Tailored Structure?������������������������������������������������  61 5.2Proposition for a “One-Size-Fits-All” ������������������������������������������������  63 5.3The Profit Framework ������������������������������������������������������������������������  64 5.4The Pricing Framework����������������������������������������������������������������������  65 5.5Operations ������������������������������������������������������������������������������������������  66 5.6Growth and Innovation ����������������������������������������������������������������������  67 5.7Mergers and Acquisitions ������������������������������������������������������������������  68 5.8New Ventures and Startups ����������������������������������������������������������������  70 6Principles of Data Science: Primer����������������������������������������������������������  73 6.1Basic Mathematic Tools and Concepts ����������������������������������������������  75 6.2Basic Probabilistic Tools and Concepts����������������������������������������������  81 6.3Data Exploration ������������������������������������������������������������������������������    84 Detailed Contents xiii 7Principles of Data Science: Advanced����������������������������������������������������   87 7.1Signal Processing: Filtering and Noise Reduction����������������������������    88 7.2Clustering������������������������������������������������������������������������������������������    91 7.3Computer Simulations and Forecasting��������������������������������������������    93 7.3.1Time Series Forecasts ����������������������������������������������������������    94 7.3.2Finite Difference Simulations ����������������������������������������������    95 7.3.3Monte Carlo Sampling����������������������������������������������������������    99 7.4Machine Learning and Artificial Intelligence ����������������������������������   102 7.4.1Overview of Models and Algorithms������������������������������������   102 7.4.2Model Design and Validation�����������������������������������������������   109 7.4.3Natural Language Artificial Intelligence������������������������������   112 7.5Case 1: Data Science Project in Pharmaceutical R&D��������������������   115 7.6Case 2: Data Science Project on Customer Churn����������������������������   122 8Principles of Strategy: Primer����������������������������������������������������������������  129 8.1Definition of Strategy������������������������������������������������������������������������   129 8.2Executing a Strategy ������������������������������������������������������������������������   130 8.3Key Strategy Concepts in Management Consulting ������������������������   130 8.3.1Specialization and Focus������������������������������������������������������   130 8.3.2The Five Forces��������������������������������������������������������������������   131 8.3.3The Value Chain and Value Network������������������������������������   133 8.3.4Integration ����������������������������������������������������������������������������   135 8.3.5Portfolio Strategies����������������������������������������������������������������   136 8.3.6Synergy ��������������������������������������������������������������������������������   138 8.3.7The Ansoff Growth Matrix����������������������������������������������������   139 8.3.8Innovation Strategies������������������������������������������������������������   140 8.3.9Signaling ������������������������������������������������������������������������������   144 8.4Marketing Strategies ������������������������������������������������������������������������   146 8.4.1Customer Segmentation��������������������������������������������������������   146 8.4.2Market Analysis��������������������������������������������������������������������   147 8.4.3Competitive Analysis������������������������������������������������������������   148 8.4.4Positioning����������������������������������������������������������������������������   149 8.4.5Benchmarking ����������������������������������������������������������������������   151 9Principles of Strategy: Advanced ����������������������������������������������������������  153 9.1Functional Strategy ��������������������������������������������������������������������������   153 9.1.1Performance Strategies ��������������������������������������������������������   153 9.1.2Quality Management������������������������������������������������������������   156 9.1.3Operation Strategies��������������������������������������������������������������   158 9.1.4Information Technology Strategies��������������������������������������   159 9.1.5Turnaround Strategies ����������������������������������������������������������   161 9.1.6Downsizing Strategies����������������������������������������������������������   162 9.2Business Strategy������������������������������������������������������������������������������   162 9.2.1Marketing Strategies ������������������������������������������������������������   162 9.2.2Small Business Innovation Strategies ����������������������������������   166 9.3  Corporate Strategy 169 Unfortunately many M&A initiatives fail to deliver expected results [263], often because the price was too high or because culture clashes occur between the two organizations (in term of management style, values, priorities) The phenomenon of culture clash is particularly frequent in M&A between a large conglomerate and a small player [264] Lesson learned, the consultant should thus inquire and give top executives some ideas of how things look from the perspective of the other organization An M&A assignment may be structured in five phases: Articulate capabilities that the client needs in view of its short- and long-term objectives, product line and market demand (capability gaps) Analyze cost-benefits of developing the capability internally vs acquiring it from outside Evaluate different targets by examining financials and strategic fit (synergies) with regards to marketing, production capacity, staff capabilities, organizational culture, management style Formulate a transition plan, including lines of report and performance targets Monitor the plan to evaluate progress and enable the client to make adjustments 9.3.4 Collaboration, Cooperation (and Coexistence…) Strategies Networks are spreading globally [265] as an effective tool to improve companies’ productivity by focusing on the things they best Networks help companies stay tune to the changing and diverse needs of their customers and rapidly transform their supply, production and distribution systems In the high-tech industries (e.g microprocessors, communication), an emerging phenomenon of coexistence between competitors in a same marketplace has been documented [266] By cooperating, competitors can improve their productivity and competitiveness by easier access to innovation and new technology, and can share risks and liabilities The revolution in the economics of information brings the concept of signaling to unprecedented new levels Examples of coexistence between competitors now include contract manufacturing between electronic firms, network intelligence between IT firms, network incubator between startups, partners, advisors and venture capital firms and virtual continuous teamwork between partners spread across different geographic locations and different time zones Conclusion The goal of this book was to overview all major aspects of the management consulting industry, including elements that are expected to become core to the value proposition in the near future It aimed at a “scientific” introduction to management consulting and data science and at discussing the emerging role of information technologies in consulting activities Again the reader is invited to consider these facts, models, tools (i.e both elementary and more advanced) and suggestions as a simple aid to thinking about reality, a backbone to facilitate discussion and creativity over concrete issues Traditional consulting activities have already been augmented by computer-­ enabled methodologies, and have already moved from a primarily judgment-based to a more process-based value proposition Given the recent disruptive changes in the “economics of information” across nearly all industries, it is fair to believe that this trend will continue and that the role played by computational data analysis in management consulting will also increase further Because computer-based analytics (a.k.a big data) flourished only 4–5  years ago, covering and blending together the essential “qualitative” concepts of management consulting with the essential “mathematical” concepts of data science has never been attempted in just one book The intention of this book was clear thus But the result is incomplete Even though the bridge between management sciences and data sciences is evolving fast, it is unfinished, and the interested reader should develop it further on its own There is no standard or best practice for this endeavor just yet, and clearly some links are still missing between data science and management consulting But again, the goal of this book was a complete overview of management consulting and relevant data science concepts, not a simple toolkit to apply blindly The next step for the interested reader thus, is what professional consultants and data scientists are already doing, albeit separately: choose between management consulting or data science, or a specific subject matter, and study this topic in isolation Then choose another one This works Now if this book achieved its goal, what you might have considered highly technical or complex earlier is now part of your comfort zone, or at least out of the unknown And this will make your next step much easier to undertake So for this next step… © Springer International Publishing AG, part of Springer Nature 2018 J D Curuksu, Data Driven, Management for Professionals, https://doi.org/10.1007/978-3-319-70229-2 171 172 Conclusion You will find hundreds of references in the bibliography categorized per chapter, meaning that material on overall industry, IT disruption, client-consultant relationship, data science and strategy for example can be found in Chaps 1, 2, 4, 6, 7, and 9, respectively To be more concise and offer my opinion, below is a quick selection For further reading on the IT disruption: Consulting on the Cusp of Disruption from Clayton Christensen et al is a visionary article on how computer-based analytics may eventually disrupt the management consulting industry For further reading on the client-­ consultant relationship: The critical success factors in the client-consultant relationship from Steven Appelbaum is also a visionary article as it first describes success factors from survey results but then leverage the data analytics method of “Machine Learning” to select and prioritize the most relevant factors For further reading on data science: Naked Statistics from Charles Wheelan is one of the best book ever written on statistics because it covers all essentials in data science using an applied, pedagogic approach that anyone who can read can understand And if you have a technical background you will still find there a refreshing overview of fundamentals written in an elegant and fun language Finally, for further reading on strategy and traditional management consulting activities: Management Consulting from Milan Kubr is a reference, with 1000 pages that run through the different types of traditional consulting approaches The accompanying website econsultingdata.com is a resources platform in management consulting that can be accessed from the MIT consulting club’s website and a few other partner websites It contains original contents as interactive versions of some parts of the book (e.g industry snapshots of Chap 1, consulting toolbox of Chap 3), but also redirects toward books, articles, tutorials, reports and consulting events It is a simple way to leverage the extensive list of references contained in this book, and to thereby pursue your own literature review References Evans C, Holmes L (2013) Re-tayloring management: scientific management a century on Gower Publishing, Farnham McAfee A (2012) Big data: the management revolution Harv Bus Rev 90(10):60–68 Kiechel IIIW (2010) The lords of strategy Harvard Business School Press, Boston Christensen CM, Wang D, van Bever D (2013) Consulting on the cusp of disruption Harv Bus Rev 91(10):106–150 Kubr M (2002) Management consulting  – a guide to the profession International Labor Organization, Geneva Graham M (2012) Big data and the end of theory? 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Portfolio management for new products Perseus Books Group, Cambridge 263 Kummer C, Steger U (2008) Why merger and acquisition (M&A) waves reoccur: the vicious circle from pressure to failure, strategic Manag Rev 2(1):44–63 264 Thomas RJ (2000) Irreconcilable differences Accenture Outlook 2000:29–35 265 Castells M (2011) The rise of the network society: the information age Wiley, Malden 266 Zhang H, Shu C, Jiang X, Malter AJ (2011) Innovation in strategic alliances: a knowledge based view In: Marketing theory and applications, 2011 AMA Winter Educators Conference, pp 132–133 Index A Ablative analysis, 110 Analytics, 11–13, 17–26, 34, 38, 77, 82, 87, 88, 90, 91, 93, 99, 111, 146, 159, 171, 172 Ansoff matrix, 139–140 Arbitrage, 96 ARIMA/auto-regression, 94 Artificial intelligence, 87, 102–115, 159 Association measure, 77 B Bayesian inference, 83, 102 BCG matrix, 136–138, 167 Benchmarking, 40, 41, 133, 151–152, 155, 158 Big data, 2, 13, 18–26, 38, 74, 86, 146, 159, 171 Biomarkers, 37, 115 Black–Scholes, 96, 99 Blue Ocean, 29, 66, 71, 129, 133, 141, 143, 144, 148 Boundary conditions, 93, 95, 96, 98 Brainstorming, 23, 29, 31, 36, 55, 155, 156 Business strategy, 2, 38, 159, 160, 162–166 Butterfly effect, 106, 110 C Central limit theorem, 83, 100 Classifiers, 113, 114, 120, 126 Client-consultant relationship, 1–2, 14, 27, 33, 46, 47, 49, 50, 54, 172 Clustering, 62, 87, 91–93, 102, 106, 109 Coefficient of determination, 79 Coexistence, 7, 133, 154, 169 Competitive analysis, 65, 148–149 Competitive intelligence (CI), 15, 16, 27, 39–41 Complexity tradeoff, 80, 109 Computational linguistic, 112 Computer simulations, 84, 87, 93–102, 111 Confidence interval, 81–83, 100, 107 Confusion matrix, 109, 120, 122 Consulting deck, 56–59 Consulting engagement, 122 Consulting framework/case structure, 50, 61, 63 Consulting sectors, Consulting services, 1–3, 10–12, 17, 18, 20, 47, 166 Consulting tools/toolbox, 41, 172 Control variate, 101 Corporate strategy, 167–169 Correlation-causation fallacy, 35 Correlation measure, 77 Cost optimization, 31, 32, 87 Cross-validation, 75, 76, 109, 115, 120, 121, 124 Customer churn, 87, 122–127 Customer segmentation, 34, 144, 146–148, 151 D Data analysis, 21, 25, 74, 81, 84, 86, 87, 160, 171 Data exploration, 75, 84–86, 102 Data reduction, 87 Data science, 19, 21, 38, 86, 90, 102, 107, 115–127, 171, 172 Deep learning, 95, 107–109 Descriptive statistics, 34, 75 Diffusion equation, 95 Disruptive innovation, 19, 29, 66, 71, 129, 133, 138, 141–144, 148, 167 Diversification, 41, 54, 68, 69, 135, 140 Documentary research, 25, 26, 33–34 Downsizing, 66, 162 Due diligence, 28, 29, 40, 41, 54, 68, 69 Dynamic state, 95 © Springer International Publishing AG, part of Springer Nature 2018 J D Curuksu, Data Driven, Management for Professionals, https://doi.org/10.1007/978-3-319-70229-2 183 Index 184 E Eigenvalues, 88–91 Eigenvectors, 89–91 Energy sector, 148 Ensemble modeling, 124 Ergodicity, 98 Error measure/loss function, 80, 106, 109, 110, 114, 121, 123 F Feature selection, 83, 109, 110, 115, 124, 126 Financial services, 4, Finite difference, 93, 95–101 Focus, 2–4, 11, 12, 14, 18, 22, 25, 27, 32–34, 36, 37, 40, 41, 44, 55, 66, 70, 74, 82, 87, 129–131, 135, 142, 144, 148, 154, 156–158, 160–162, 167, 168 Focus group, 25, 26, 31, 34, 36–37 Forecasting, 35, 87, 93–102 Fourier Transform, 91 G Growth strategy, 33, 65, 67, 133, 139, 140, 142, 144, 167 H Harmonic analysis, 88, 90, 91, 110 Healthcare, 5, 11, 115, 148 Hierarchical clustering, 92 Horizontal integration, 133, 135 Hypothesis function, 109, 119, 120 Hypothesis testing, 81, 82, 102 I Imbalanced dataset, 122 Importance sampling, 101 Inductive reasoning, 19, 56, 58, 59, 62 Information technology (IT), 12, 13, 17, 20, 21, 24, 30, 32, 49, 59, 67, 151, 154, 159–160, 163–166, 169, 172 Information technology strategy, 49, 159–160 Innovation strategy, 140–144, 148, 166 Insurance sector, Interviewer effect, 36, 37 Interviewing, 18 J Jobs-to-be-done, 3, 10, 29, 34, 36, 142, 147, 163, 167 K K-folding, 75, 76, 80, 103, 109 K-means/partitional clustering, 92 L Least square approximation, 78 M Machine learning, 50, 74, 75, 77, 85, 87, 91, 102–115, 120–124, 159 Management consulting, 1, 2, 9–13, 15, 16, 26, 27, 30, 45, 51, 53, 62, 87, 93, 122, 130–147, 159, 160, 171, 172 Manufacturing sector, Market analysis, 138, 147–148 Marketing strategies, 131, 146–152, 162–166 Market research, 15, 18, 22, 27, 28, 31, 33–39, 115, 163 Markov property, 93, 101 Media sector, Mergers and acquisitions (M&A), 3, 6–9, 28, 41, 63, 66, 68–69, 135, 138, 168, 169 Mixing, 90, 94, 95 Mode effect, 37 Model design, 82, 86, 109–112, 123, 124 Models of client interaction, 44 Moment matching, 101 Monte Carlo, 93, 99–102 Mutually Exclusive and Collectively Exhaustive (MECE), 57, 61, 62 N Natural language processing (NLP/NLU), 34, 105, 107, 112, 114 Neural network, 102, 105–108, 113 Newton method, 80, 99, 106 New ventures, 3, 12, 63, 70–71 O Operation strategy, 158 Option pricing, 4, 96, 100 Organizational development (OD), 15, 16, 27–33, 45 Outsourcing, 24–26, 32, 40–41, 133, 159, 160, 166, 168 P Performance strategy, 153–156 Pharmaceutical, 12, 87, 115–122, 150 Index Porter 5-forces, 132, 136 Portfolio strategy, 136–138, 167–168 Positioning, 3, 136, 149–152, 167 Pricing, 3, 4, 34, 38–39, 63, 65–66, 70, 139, 150, 164 Principal component analysis (PCA), 88–91, 102, 109, 110 Prisoner dilemma, 145, 146 Probability theory, 75, 81 Product life cycle, 129, 138, 147, 148, 154 P-value, 74, 81–83, 107, 110, 116, 119, 120 Q Quality management, 153, 156–158 R Random forest, 105–107, 121, 122 Randomness, 93 Receiver-operator curve (ROC/AUC), 124 Recurrent neural network, 107, 113 Reengineering, 32, 39, 67 Regression, 77–81, 94, 102, 103, 105–107, 109, 110, 113, 115–117, 119, 120, 122, 124, 126 Reinforcement learning, 107, 114 Replica exchange, 101 Resource allocation, 28, 31–32, 34, 129, 154, 167–168 S Scenario planning, 13, 22, 23, 26, 28–31, 39, 54 Selection bias, 35, 84 Sensitivity analysis/scenario planning, 123, 126–127 Sentiment analysis, 114 Signaling, 144–146, 169 Signal processing/filtering, 88–91, 111 Singular value decomposition (SVD), 88–90, 110 Small business, 166 Specialization, 3, 130–131, 133, 143 185 Standard deviation, 75, 76, 81, 82, 94, 96, 97, 100, 101, 123 Standard error, 75, 82, 100 Stationarity, 94, 98 Stepwise regression, 106, 110, 115–117, 119, 124 Stochastic process, 93, 94, 99, 100 Strategic planning, 1, 28, 41, 156 Strategy, 2, 4, 11, 24, 28, 35, 40, 57, 64, 65, 68, 73, 127, 129–152, 169 Stratified sampling, 35, 36, 84 Supervised learning, 114 Supply chain management, 25, 26, 39–41, 158 Support vector machine (SVM), 105–107, 109, 122, 126 Surveys, 25, 26, 34–37, 39, 49, 50, 57, 71, 73, 80, 84, 85, 152, 154, 172 Sustaining innovation/incremental innovation, 29, 141, 144 Synergies, 29, 41, 54, 68–70, 123, 127, 135, 138–140, 150, 167, 169 T Telecommunication sector, Time series analysis, 95 Turnaround strategy, 161, 162 U Unsupervised learning, 113 V Value chain/supply chain, 3, 8, 25, 39–41, 129, 133, 135, 139, 141, 146, 157–160 Value innovation, 143 Value network, 3, 14–16, 66, 70, 71, 86, 133–135 Vertical integration, 32, 135, 158 W White noise, 94, 100 ... approaches in management consulting The business of management consulting has always been about gathering data, analyzing it, and delivering recommendations based on insights gathered from the analyses... expansion into BRIC countries and most specifically China and India Increased demand for management consulting in emerging economies is significantly boosting the industry and expected to continue... system management, inventory management, scheduling, warehousing, and even entire business model re-design 10% – Financial management This segment includes services in banking, insurance and wealth
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